Super-Resolution Direction-of-Arrival Estimation with Atomic Norm Minimization

Abstract

When a mixture of radiating sources and noises impinges on an antenna array, Direction-of-Arrival (DOA) estimation of source signals from the sensors array in presence of noise has attracted a lot of attention. Accurately estimating the DOAs of signals from the noisy observations is a challenge. With the development of sparse representation theory, atomic norm minimization (ANM) provides a super-resolution algorithm to denoise the observations of the sensors. Because of the convexity of the atomic norm, ANM problem can be solved by convex optimization, and DOAs of signals can be found from the dual polynomial of the prime problem. The problem is solved by Alternating Direction Method of Multipliers (ADMM) reasonably efficiently. Several simulations are conducted to verify the favorable performance of the proposed method by comparison with several existing methods such as MUSIC and Cadow’s methods, and the results demonstrate practical ability of the proposed method in both model order and DOA estimation in the presence of light noise.